Course syllabus for Applied control system design

The course syllabus contains changes
See changes

Course syllabus adopted 2021-02-19 by Head of Programme (or corresponding).

Overview

  • Swedish nameTillämpad reglerdesign
  • CodeSSY251
  • Credits7.5 Credits
  • OwnerTIMEL
  • Education cycleFirst-cycle
  • Main field of studyAutomation and Mechatronics Engineering, Electrical Engineering
  • DepartmentELECTRICAL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 67116
  • Maximum participants70
  • Open for exchange studentsYes

Credit distribution

0120 Laboratory 1.5 c
Grading: UG		 1.5 c
0220 Written and oral assignments 1.5 c
Grading: UG		 1.5 c
0320 Examination 4.5 c
Grading: TH		 4.5 c
  • 08 Jan 2022 am J
  • 11 Apr 2022 am L
  • 26 Aug 2022 pm L

In programmes

Examiner

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Eligibility

General entry requirements for bachelor's level (first cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

The same as for the programme that owns the course.
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

The course LEU236 Dynamical systems and control engineering, or equivalent knowledge.

Aim

The course aims at giving theoretical and practical knowledge in dynamic modelling, system identification, state estimation, and feedback controller design. Special focus is on state-space models and simulation as well as on components in computer-based control systems.

Learning outcomes (after completion of the course the student should be able to)

  • model linear and nonlinear processes, as state-space models and as transfer functions.
  • analyze properties of linear systems, e.g., stability, controllability, reachability, and observability.
  • use common methods for system identification, e.g., least squares estimation and gradient algorithms, in combination with parametric modeling
  • observe state variables from noisy measurements using Luenberger observer and Kalman filter.
  • design output feedback (e.g., PID and cascade control) and state feedback controllers to achieve desired system performance.
  • use computer-based tools for simulation and design of dynamic feedback systems.
  • search effectively for information in books, journals and local databases. 
  • evaluate information with regards to relevance and quality.
  • use a process-oriented approach to writing, which includes giving and receiving constructive feedback. 
  • identify and analyse technical methods
  • use strategies to improve written proficiency in English 

Content

  • State-space models of common dynamic processes, model discretization and linearization.
  • Analysis, analytical solution, and numerical simulation of linear-time-invariant (LTI) systems.
  • Parametric optimization and parameter identification algorithms.
  • Model-based observer design to estimate state variables.
  • PID and cascade control.
  • State feedback and control system design using the pole placement method.
  • Some on multivariate control, feedforward, and linear quadratic regulator (LQR).
  • Computer exercises and labs in computer-based tools.
  • Written analysis of technical content.

Organisation

The education is given as lectures, exercises, computer laborations, laborations, and hand-in exercises. The hand-in exercises are solved in groups of two students per group.

Literature

[1] Feedback Systems: An Introduction for Scientists and Engineers. Karl Johan Åström and Richard M. Murray, Princeton University Press, 2008.
[2] Optimal State Estimation: Kalman, H1, and Nonlinear Approaches (Chapters 3 and 5). Dan Simon, John Wiley & Sons, 2006.
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[1] Feedback Systems: An Introduction for Scientists and Engineers. Karl Johan Åström and Richard M. Murray, Princeton University Press, 2008.
[2] Optimal State Estimation: Kalman, H1, and Nonlinear Approaches (Chapter 3 and 5). Dan Simon, John Wiley & Sons, 2006. 

Examination including compulsory elements

Approved laboratory work, project assignment, and written exam. Possibly additional assignments for bonus points. Final grades are given on a scale of 3-5 based on results from the written exam.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

The course syllabus contains changes

  • Changes to examination:
    • 2021-06-22: Examination datetime Examination datetime 2022-01-08 Morning added by M Stosic
      [4,5 hec, 0320]
    • 2021-06-22: Location Location Johanneberg added by M Stosic
      [2022-01-08 4,5 hec, 0320]